Abstract:The degradation of the power semiconductor gate oxide layer has a serious impact on its normal operation. Different bias stress lead to different forms of gate oxide degradation. Therefore, the condition monitoring research of the gate oxide layer is of great significance to power semiconductor’s high reliability. Starting from the microscopic mechanism of gate oxide degradation, establishing an accurate function relationship between related characteristic parameters and gate oxide degradation. When the gate oxide layer is degraded, the drift of the characteristic parameters can be detected to realize the condition monitoring of the power semiconductor gate oxide layer. Focusing on the domestic and foreign research on degradation characteristic parameters and degradation models, the latest progress on condition monitoring methods based on threshold voltage and Miller effect parameters are discussed in classification, and the performance of gate oxide condition monitoring methods are analyzed and compared. On this basis, combined with the development and application trends of power semiconductor, the research direction of gate oxide condition monitoring of power semiconductor devices is prospected.

Abstract:Quantum Hall effect provides the basis for primary resistance standard in electrical metrology. With discovery of new material and development of new technology, new progress is seen in quantum Hall resistance standard (QHRS). The development of QHRS is reviewed, starting from description of discovery of quantum Hall effect and its theory and universality, followed by introduction of GaAsbased and graphenebased quantum Hall devices and quantum resistance array, advantages of the new graphenebased QHRS and its development status are introduced, different types of resistance bridges, including direct current comparator, cryogenic current comparator and low frequency current comparator, are summarized, and development trend of QHRS is prospected.

Abstract:Regular stress detection for the ferromagnetic components in the industrial field can ensure the safety of industrial equipment. This paper proposes a method of steel stress measurement based on magnetoelastic effects under weak alternating current (AC) induction magnetic field. Firstly, the magneticstress relationship of steel plates under weak AC magnetization condition is studied through finite element simulations and the position of the measuring point is optimized. Then the stress measurement experiment for different thermomagnetic treated steel plates is carried out under weak AC magnetization condition. Results demonstrate that the normal weak AC induction magnetic field near the steel plate surface under different frequencies has noticeable stresssensitive characteristics, when the elastic stress varies from 0 MPa to 167 MPa, the normal weak AC magnetic field amplitude on the surface of the steel plate has an approximately linear monotonic relationship with the stress, demagnetization and heat treatment can effectively improve the consistency of the stressmagnetic relationship of the same size steel plate, the weak AC magnetization condition has noncontact, good repeatability and ability of stress measurement to resist background magnetic interference.

Abstract:The triplecoil inductive debris sensor is the main sensor for the monitoring of metal debris in lubricating oil. But the bubble in the lubricating oil will make the sensor produce interference signal. Aiming at the problem that the traditional method mistakenly detects the bubble interference signal as the metal debris signal, a signal detection method based on continuous wavelet transform and curve fitting is proposed. Taking advantage of the high similarity between the metal debris signal and Gaussian1 wavelet, the continuous wavelet transform (CWT) threshold method is used in wavelet domain to filter interference signal and extract signal waveform, the Gauss Newton method is used to fit the extracted signal waveform, and the Rsquared is used as the standard to judge the fitting results, detect the metal debris signal and remove the interference signal. The actual test on the sensor with an inner diameter of 20 mm shows that it can accurately detect the spherical ferromagnetic debris with a diameter of more than 150 μm and the spherical copper debris with a diameter of more than 250 μm, with an accuracy of 99% and 97% respectively, and can effectively remove the bubble interference signal.

Abstract:The ampacity of submarine cables is an important parameter for its operation and scheduling, which is restricted by the maximum allowable operating temperature of submarine cables. It is of great significance to establish the temperature field and ampacity model of submarine cables considering the influence of ocean currents in complex submarine environment. In view of the complicated calculation of the traditional analytical method and just suitable for specific laying environment and other problems, according to the electroheatflow multifield coupling theory, based on the finite element analysis technology, the temperature field and ampacity analysis model of highvoltage threecore XLPE submarine cable under the two laying methods of burying and laying are established respectively, and studied the influence of ocean current velocity and temperature on the ampacity. The research results show that the relative error between the proposed model with the traditional analytical method is within 5%; the steadystate ampacity of the laid cable is 200~330 A higher than the buried under the same environmental factors, and the change rate of the two methods is more sensitive at low current rates, but it is not affected by the temperature of the ocean current. In addition, the allowable operating time of shortterm emergency ampacity is inversely proportional to its size and initial cable core temperature.

Abstract:The large water flow standard facility is the core device for carrying out large pipe diameter flow meter test and ensuring the accuracy and reliability of traceability and transmission of large water flow value, and the diverter is the most important component which affects the accuracy and reliability of the measurement results. Aiming at the limitations of the existing facilities for large water flow, such as difficult stability assurance, large reversing error, poor dimensional adaptability of the tested instrument and low test efficiency, a set of large diameter water flow standard facility with high accuracy, high velocity and a new reversing system were designed and developed. The facility has three principles of static mass method, volume method and standard meter method at the same time, and the maximum test pipe diameter is DN1000, the maximum operating flow rate is 10 000 m3/h, the local maximum flow velocity can reach 10 m/s, the overall uncertainty is better than 02% (k=2). The law of liquid quality change during the reversing process in the static mass method large flow standard facility was analyzed, and the relation model between reverse flow error and timing error is established, and finally the uncertainty evaluation test of this new type of diverter was carried out, the results show that the timing uncertainty is better than 883×10-6 and the total uncertainty of measurement time is better than 924×10-6, and the magnitude is very small, so the performance of the diverter is excellent.

Abstract:At present, long test time and low test efficiency are one of the key problems affecting test cost in IC testing. To solve this problem, a test set reordering method based on test performance estimation is proposed. Firstly, different fault types are classified and modeled, and then each fault type is simulated. The test performance of test patterns is estimated by injecting faults into each logic gate and counting the total area of test patterns hitting the fault gate. Finally, the test sets are reordered according to the test performance. Experiments show that the sequenced test set test can reduce the fault detection time by 5329% for single stuckat fault. This method is to analyze and count the logic structure of the circuit and then optimize the test set, test the ISCAS 89 standard circuit, and compare it with other test sets to reorder it, which has obvious optimization. The algorithm operation is completely softwarebased, without any additional hardware overhead, and can be directly compatible with the traditional integrated circuit test process.

Abstract:Aiming at the problems that traditional deep learning methods do not make full use of the timing characteristics of bearing signals, and are difficult to process dynamic data, an improved CNNBiGRU intelligent diagnosis method for bearing faults is proposed. The convolutional neural network is used to extract representative features from the input signal, and the bidirectional gated recurrent neural network is introduced to mine the semantic information in the time dimension of the fault data, and the attention mechanism is used to adaptively assign different weights to the feature map to achieve high precision diagnosis of bearing faults. Experiments on public bearing data sets show that the method can correctly classify bearing faults with a classification accuracy of 996%。

Abstract:For data storage and node repair in distributed storage systems, heterogeneous fractional repetition (FR) codes are constructed based on mixed orthogonal array. It is proved that the constructed heterogeneous FR codes are universally good generalized fractional repetition (GFR) codes. Concretely, the incidence matrix of FR codes is obtained by using the horizontal pairs in the mixed orthogonal array, and the data blocks are stored in the nodes of distributed storage systems. In addition, the grouping method is used to construct the grouping FR codes on the basis of the mixed orthogonal array, realizing the precise noncoding repair of a single fault node within the local repair group, and the repair locality is 2 or 3. Moreover, the grouping FR codes can repair multiple fault nodes quickly and efficiently. Performance analyses and experimental simulations show that, compared with RS codes and simple regeneration codes, the constructed grouping FR codes have lower repair bandwidth overhead and repair locality, and the repair efficiency is also improved.

Abstract:In this paper, a diamagneticairflow hybrid levitation structure is reported, with a levitated rotor stably levitated above a diamagnetic disc just using a lifting magnet and airflow. The levitation characteristics were studied through theoretical analysis and finite element analysis (FEA) method. And driving experiment was carried out using compressed nitrogen as driving source. The rotation speed of the levitated rotor is 16 666 r/min, and the levitation gap is 07 mm when the airflow rate is 2 198 sccm under standard temperature and pressure. At the same time, the levitated rotor can be stably levitated and rotated at any height from 02 mm to 08 mm by adjusting the vertical position of the nozzles. When the airflow rate reaches 2 748 sccm, the speed of the levitated rotor is 22 300 r/min. With the hybrid levitation structure, the levitated rotor has a large levitation gap and a high rotation speed. This levitation structure is expected to be applied to sensing, energy harvesting and air bearing under actuation of airflow.

Abstract:Due to the complexity of the water system, it is difficult to establish an ideal nonlinear system with traditional water quality prediction methods. In order to improve the accuracy of water quality prediction, this paper proposes a water quality prediction model that uses the fruit fly algorithm (fruit fly optimization algorithm (FOA) to improve the generalized neural network (general regression neural network (GRNN). Using the global optimization feature of the fruit fly optimization algorithm that can optimize the key parameters, combined with the highprecision approximation ability of the generalized neural network, the FOAGRNN water quality prediction model is established. Four items of data including oxygen content, temperature, total nitrogen, and total phosphorus collected from observation station No.0 in Taihu Lake are selected, and the data are preprocessed and simulated by linear interpolation and normalization. The simulation results show that, compared with the GRNN model and the BP model, the prediction results of FOAGRNN are closer to the true value. The root mean square errors of the four prediction indicators are 0164 83, 0250 39, 0126 59, and 0111 19, respectively, which are all lower than the GRNN model and the BP model have the advantages of strong stability and high accuracy, and have great practical application value in water quality prediction.

Abstract:In view of the fact that the impact signal of rolling bearing is easily submerged by noise and nonstationary characteristics, and the problem that the effective information in the IMFs cannot be fully utilized when the traditional CEEMDAN is used, a rolling bearing fault feature extraction method based on weighted average complete ensemble empirical mode decomposition with adaptive noise (WACEEMDAN) and modulation signal bispectrum (MSB) for rolling bearings is proposed. First, the CEEMDAN is used to decompose the collected nonstationary vibration signals into several inherent modal functions with stationary characteristics IMFs. Then, a new type of index is constructed, which emphasizes sensitive component: correlationkurtosis value, the index is used to weight each IMFs and reconstruct it into WACEEMDAN signal. Finally, the MSB is used to decompose the modulation components in the WACEEMDAN signal and extract the fault characteristic frequency. The results show that: by using the general bearing data set of Xi’an Jiaotong University and our test bench, the proposed WACEEMDANMSB method can accurately extract the characteristic frequency of bearing faults, thus, verify the effectiveness of the WACEEMDANMSB method.

Abstract:Joint data combined with convolutional neural network for twoperson interaction behavior recognition has the problem of insufficient expression of interactive information during the imaging process and ineffective modeling of timeseries relations. In combination with recurrent neural network, there is a problem that focuses on the representation of time information. However, it ignores the problem of constructing information about the spatial structure of the twoperson interaction. Therefore, a novel model named CNN attentionbidirectional long shortterm memory (CNN ABLSTM) network is proposed. First, the joints of each person are arranged based on the traversal tree structure, and then the interaction matrix is constructed for each frame of data in the video. The values in the matrix are the Euclidean distance between the arranged joint coordinates of two persons. After encoding the grayscale image of the matrix, the images are sequentially sent to CNN to extract deeplevel features to obtain the feature sequence. And then the obtained feature sequence is sent to the ABLSTM network for time series modeling, and finally sent to the Softmax classifier to obtain the recognition result. The new model is applied to 11 types of twoperson interaction in NTU RGB D dataset, and the accuracy is 90%, which is higher than the current twoperson interaction recognition algorithm. The effectiveness and good generalization performance of the new model are verified.

Abstract:Brushless DC motors (BLDCMs) have been intensively used in automobiles, industry automations, and aeronautics and astronautics due to their advantages including high efficiency, high power density, and low noise. High resistance connection (HRC) fault is one of the typical motor faults. A severe HRC fault will cause serious temperature rise and even fire, and hence diagnosis of HRC fault in BLDCM is of significant. Generally, the HRC faults are detected by analyzing the motor current and voltage signals. However, there still deficiencies in the existed methods. This study designs a new method that combines the analysis of the array leakage flux signals and machine learning technology to realize location and quantitative analysis of HRC fault in BLDCM. First, multichannels of flux signals captured by a Hall sensor array that installed on the motor shell are sampled. A neural network model based on the timedomain features is designed to detect and localize the HRC faults. Subsequently, another neural network model based on the frequencydomain features is used to quantitatively analyze the HRC fault degree. The experimental results indicated that the accuracy of fault detection and localization is 9875% and the averaged root mean square error of quantitative analysis is 0018 Ω. The proposed method is noninvasive, easy to implement with high efficiency, hence, it will improve the accuracy and efficiency of HRC fault detection in BLDCM.

Abstract:In industries for gas turbine rotor, there are a large number of normal operation vibration signal sample data and few fault data,which caused fault diagnosis accuracy lower. A gas turbine rotor deep transfer learning fault diagnosis method is proposed. First, a firstlayer wide convolutional kernel deep convolutional neural network (WDCNN) model is pretrained with a typical industry sample dataset, obtained the model initial weights. Second, in the source domain, the weights of the WDCNN model are updated using a large number of normal operation samples obtained from the test drive of a certain type of gas turbine; In the target domain, the normal and fault data sample characteristics of the gas turbine are extracted by using the convolutional layer trained in the source domain, and then the support vector machines (SVM) are used for classification identification, so as to achieve the gas turbine fault identification. The experimental results of the test data show that the method identification accuracy is 96%, which verifies the feasibility of migrating the pretrained deep learning model of the bearing dataset to the field of gas turbine rotor for fault diagnosis.

Abstract:The failure modes of electronic systems are various due to the complex functional structure and operation under complex environmental conditions of multiload coupling. Therefore, the malfunction of the electronic system can be regarded as the result of the mutual combination of multiple failure modes. At present, there are few reliability modeling methods considering the multimode coupling of electronic systems. This paper studies the reliability modeling method under the condition of multimode coupling of the electronic system. First, each failure mode's life distribution information is obtained through the reliability simulation analysis method based on the physics of failure. Then the Copula function is utilized to establish the reliability model of the multimode coupling system, and the reliability calculation method is given. Finally, a case study of a specific type of integrated electronic system shows that the method proposed in this paper can achieve more accurate calculation results of reliability, and avoid overly conservative in reliability assessment.

Abstract:Retinal vessel segmentation is an important step for automatic screening of diabetic retinopathy. Currently, most deep learning methods use a large number of labeled samples for network training, but it is difficult to obtain labeled samples in the medical field, and healthy samples and patient samples are imbalanced. In this paper, we have proposed a method of retinal vessel segmentation using few samples based on generating adversarial network. In the generator part, after preprocessing the image by inversing color and other methods, the dataset is expanded by rotation. The UNet structure is used in the network part and the discriminator uses CNN network. In the experimental stage, the training test was applied to DRIVE dataset and HRF dataset. Only 6 samples of the training set were used in the training step, and all the test samples were used in the test step. Finally, the area under the ROC curve of the two datasets reached 097 and 095, and the accuracy rate reached 095 and 094. Compared with UNet in condition of few samples, segmentation performance is improved greatly. It shows that this method is effective for the task of low sample segmentation in retina vessel.

Abstract:In view of the poor fault tolerance of the SINS/GPS/DVS fullsource integrated navigation system under nonideal conditions, and the inability to optimize the multisource integrated navigation system, a Kalman filterbased local filter for the navigation subsystem and a federation are established. The global filter of the allsource navigation system of the filter has designed an allsource faulttolerant integrated navigation system. The simulation verification shows that the allsource navigation system can achieve highprecision navigation in the case of subsystem failures, and meet the requirements of critical space aircraft for the accuracy and reliability of the navigation system.

Abstract:The open constraint condition between the train and the track determines the objective existence of the train derailment. Curve segmentation of the edge of wheelrail contact area is of great significance to the research of the train wheelrail contact relationship. In this paper, an algorithm for the curve segmentation of the edge of wheelrail contact area based on generative adversarial networks is proposed. By introducing the residual module into the generator network, the sensitivity of the network to output changes is enhanced, and the generator weight can be better adjusted. In addition, in order to effectively expand the receiving area of the feature map, the expansion residual module is introduced. The experimental results show that the accuracy of curve segmentation of the edge of wheelrail contact area reaches 9613% by improved generative adversarial networks, and the sensitivity, specificity, F1 value and area under the ROC curve is 8390%, 9713%, 8367% and 9812% respectively, which verify that this method can accurately segment the edge curve of the wheelrail contact area.

Abstract:Aiming at the problems of intricate model and low accuracy in online torque estimation of switched reluctance motor (SRM), a novel online torque estimation method based on piecewise analytical modelling is proposed. According to the symmetry of the flux linkage characteristics and the structural characteristics of the stator and rotor pole arc of the switched reluctance motor in one electrical cycle, a method of dividing the half electrical cycle into five intervals is proposed, and the flux linkage analytical model and torque analytical model of each interval are established respectively. The estimation accuracy and operation time of the above models are verified by finite element analysis and constructing an experimental system based on DSP28335. The results show that compared with the traditional single analytical model, the proposed partition analytical model not only effectively improves accuracy of torque estimation, but also significantly reduces the operation time, hence, it is helpful to improve the control accuracy and dynamic performance of switched reluctance motor speed control system, which has good application value.

Abstract:Aiming at the common carrier frequency offset phenomenon in 5G downlink systems, an improved joint frequency offset estimation method based on DMRS is proposed. Firstly, the frequency offset is roughly estimated by CP correlation in the time domain, and compensated when the frequency offset is greater than 15k Hz; Then in the frequency domain, in order to reduce the influence of channel noise and fading, the crosscorrelation between the DMRS extracted on the receiving end and the original DMRS generated locally are calculated to obtain the channel response value, and then uses the DMRS channel response values of two OFDM symbols in one slot are used for fine frequency offset estimation. The method proposed enables a higher estimation accuracy when the frequency offset is ［-15 kHz, 15 kHz］, and improves the overall performance of the system, it has been applied to a 5G multichannel base station comprehensive test instruments.

Abstract:In order to solve the problem of large errors in the ranging results caused by the interference of noise when the lidar locates the echo peak. Based on the twotime Kalman filter algorithm, this paper proposes an algorithm that can effectively suppress noise. First, perform Kalman filtering on the timedomain echo, then perform the perioddomain Kalman filtering again on the peaktopeak position difference in consecutive periods, and finally map the peaktopeak position difference to the true spatial distance. Experimental results show that the distance variance after processing by the above algorithm is reduced to less than 6% of the denoising front error, and the average absolute error and root mean square error are reduced to about 20% to 50% before denoising, indicating the filtering algorithm designed in this paper. It can effectively reduce the influence of noise and make the ranging result more stable.

Abstract:Accurate separation of fault leakage current from residual current was a typical new data prediction problem, the methods of fault leakage current separation were scarce and the accuracy was low. In this paper, we proposed a construction strategy of small scale cross auto encoder deep network, and applied the model to separate fault leakage current from the residual current. First, two independent auto encoder networks were learned on the residual current dataset and the fault leakage current dataset respectively. Then, the feature encoding module of residual current and the feature decoding module of fault leakage current were cascaded to form a cross auto encoder network. Finally, separation mapping model of residual current to fault leakage current was obtained by using the paired residual current and fault leakage current for finetuning training of the crossauto encoder network. Experiment results showed that the average separation accuracy was 7733% when the error threshold was set to 5. When the error threshold was 15, the accuracy was up to 8867%. Obviously, the method can realize the separation of fault leakage current and provide the technical support for the design of intelligent current separation residual current protection device.

Abstract:Aiming at the problems of missing real minutiae and increasing pseudo minutiae of lowquality fingerprint, and the typical fingerprint identification algorithm is too dependent on the accuracy of minutiae, a visually constrained enhanced triangulation fingerprint recognition algorithm is proposed. First, use triangle reconstruction to obtain the enhanced triangulation set according to the extracted minutiae points; then calculate the triangle feature vector, use the decrement verification for triangle matching to determine the matching minutiae pair, and use the visual constraint optimization; finally obtain the similarity according to the ratio of the matching point so as to complete the recognition. The international standard test libraries FVC2000DB2, FVC2006DB2 and FVC2006DB3 were used for comprehensive performance comparison experiments, and the EER rates of the algorithm were 432%, 264% and 798%, respectively. Compared with the Delaunay triangulation algorithm, the modified Delaunay triangulation algorithm can reduce the EER by 128%, 171% and 283%, compared with the extended triangulation algorithm by 126%, 052% and 258%, and compared with the SIFT algorithm by 089%, 297% and 003%, respectively. The experimental results show that the proposed algorithm does not need calibration and has good adaptability to the loss of real fine nodes and the increase of pseudo fine nodes caused by lowquality fingerprints.

Abstract:In order to solve the problems of white noise and periodical narrowband interference in PD detection, a denoising method combining improved variational mode decomposition (VMD) and threshold denoising is proposed. Aiming at the problem that the VMD algorithm is difficult to choose the decomposition parameters adaptively in the practical application, the decomposition number is determined by the principle of minimum energy deviation, the penalty factor of each component was optimized by BAS, and the kurtosis criterion was used to screen the effective component, so as to eliminate the narrowband interference noise. Threshold function combined with 3σ criterion was used to remove the residual white noise in the effective component and reconstruct the effective component. Through simulation and measured signal denoising analysis and compared with lifting db4 wavelet method and EEMD threshold method, the results show that this method has better denoising effect, the denoising waveform similarity coefficient is higher after denoising, the noise rejection ratio is higher and can retain more partial discharge characteristics.

Abstract:When electricpneumatic valve positioner regulates the valve opening, large overshoot will be generated in the PID process. Natural deflating or adding antireaction will be adopted to adjust the valve position, but the system robustness and control efficiency will be greatly affected. A genetic optimization PID tuning algorithm is proposed in this paper, based on the relay feedback method to estimate the system PID parameters roughly, then the improved genetic algorithm is used to optimize the PID parameters. The proposed methods enable the system to realize control without overshoot, improve the control accuracy, and shorten the adjustment time. In the process of controlling the pneumatic regulating valve with PID parameters obtained by this system, the overshoot can be controlled under 06%, and the adjusting time can be shortened by 66% compared with the traditional PID algorithm. Simulation and experiment results show that the proposed algorithm can realize stable, accurate and fast control mode, and this idea is proposed to optimize the control strategy of electricpneumatic valve positioner.